AWS Data Engineer
Qode Open · verified Oct 24, 2026 Remote United States Full-time Mid-level Data Engineering
At a glance
Mid-level Data Engineering role at Qode. Remote, United States · full-time · no salary stated · we estimate $112,500–$160,000 across the United States.
Estimated pay
Qode has not published a salary for this role. The figure below is our estimate, not theirs.
$112,500 – $160,000
What the middle half of mid-level data engineering roles in the United States paid, among postings that did state a salary.
Worked out from 386 postings that stated pay, median $134,547. Last updated Oct 24, 2026.
RoleData Engineering
SeniorityMid-level
LocationUnited States
WorkplaceRemote
EmploymentFull-time
PostedJan 30, 2026 · 8mo ago
Role brief
dataroles summary, based on the employer's posting.
What you'll do
- Build and maintain AWS data pipelines, tuning ETL workflows for efficient transformations
- Help deliver platform modernization and move on-premise workflows to AWS while checking data quality
- Automate data movement and resolve inconsistencies through integrations and validation checks
- Test system integrations and apply cloud security and compliance controls
- Work with architects and lead developers to document manual data flows and plan automation
What you bring
- At least ten years of data engineering experience using AWS technologies such as Glue, Python, Snowflake, S3 or Redshift
- You have built scalable pipelines and optimized Snowflake transformations and ETL processing
- You have worked with both streaming and batch architectures, plus Databricks
- You know Dataiku and hold a graduate degree in computer science or a related field
Who this fits
This is a mid-level, full-time remote role for someone based in the United States. It suits an experienced AWS data engineer who can work across migration, pipeline development, data quality and cloud controls.
From the employer
Job BriefAs an AWS Data Engineer, your role will be to design, develop, and maintain scalable data pipelines on AWS. You will work closely with technical analysts, client stakeholders, data scientists, and other team members to ensure data quality and integrity while optimizing data storage solutions for performance and cost-efficiency. This role requires leveraging AWS native technologies and Databricks for data transformations and scalable data processing.
Responsibilities• Lead and support the delivery of data platform modernization projects.• Design and develop robust and scalable data pipelines leveraging AWS native services.• Optimize ETL processes, ensuring efficient data transformation.• Migrate workflows from on-premise to AWS cloud, ensuring data quality and consistency.• Design automations and integrations to resolve data inconsistencies and quality issues • Perform system testing and validation to ensure successful integration and functionality.• Implement security and compliance controls in the cloud environment.• Ensure data quality pre- and post-migration through validation checks and addressing issues regarding completeness, consistency, and accuracy of data sets.• Collaborate with data architects and lead developers to identify and document manual data movement workflows and design automation strategies.Skills and Requirements· 10+ years’ experience with a core data engineering skillset leveraging AWS native technologies (AWS Glue, Python, Snowflake, S3, Redshift).· Experience in the design and development of robust and scalable data pipelines leveraging AWS native services.· Proficiency in leveraging Snowflake for data transformations, optimization of ETL pipelines, and scalable data processing.· Experience with streaming and batch data pipeline/engineering architectures.· Familiarity with DataOps concepts and tooling for source control and setting up CI/CD pipelines on AWS.· Hands-on experience with Databricks and a willingness to grow capabilities.· Experience with data engineering and storage solutions (AWS Glue, EMR, Lambda, Redshift, S3).· Strong problem-solving and analytical skills.· Knowledge of Dataiku is needed· Graduate/Post-Graduate degree in Computer Science or a related field.· AWS S3 (data storage, export, recall)· Athena (querying data lakes)· Data pipelines (batch & near-real-time)· Integration with external systems (FHIR)· Secure data handling (KMS, Macie)· Cloud-native analytics· Multi-account, multi-region data architecture· BI integrations: Power BI, Tableau, QuickSight
Responsibilities• Lead and support the delivery of data platform modernization projects.• Design and develop robust and scalable data pipelines leveraging AWS native services.• Optimize ETL processes, ensuring efficient data transformation.• Migrate workflows from on-premise to AWS cloud, ensuring data quality and consistency.• Design automations and integrations to resolve data inconsistencies and quality issues • Perform system testing and validation to ensure successful integration and functionality.• Implement security and compliance controls in the cloud environment.• Ensure data quality pre- and post-migration through validation checks and addressing issues regarding completeness, consistency, and accuracy of data sets.• Collaborate with data architects and lead developers to identify and document manual data movement workflows and design automation strategies.Skills and Requirements· 10+ years’ experience with a core data engineering skillset leveraging AWS native technologies (AWS Glue, Python, Snowflake, S3, Redshift).· Experience in the design and development of robust and scalable data pipelines leveraging AWS native services.· Proficiency in leveraging Snowflake for data transformations, optimization of ETL pipelines, and scalable data processing.· Experience with streaming and batch data pipeline/engineering architectures.· Familiarity with DataOps concepts and tooling for source control and setting up CI/CD pipelines on AWS.· Hands-on experience with Databricks and a willingness to grow capabilities.· Experience with data engineering and storage solutions (AWS Glue, EMR, Lambda, Redshift, S3).· Strong problem-solving and analytical skills.· Knowledge of Dataiku is needed· Graduate/Post-Graduate degree in Computer Science or a related field.· AWS S3 (data storage, export, recall)· Athena (querying data lakes)· Data pipelines (batch & near-real-time)· Integration with external systems (FHIR)· Secure data handling (KMS, Macie)· Cloud-native analytics· Multi-account, multi-region data architecture· BI integrations: Power BI, Tableau, QuickSight
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